# Ollama Apify Mcp (`lenticular_negative/ollama-apify-mcp`) Actor

The Ollama MCP Actor brings together Apify’s web-scraping power with fast, private, on-device AI. No external APIs required. It lets you run local LLMs through Ollama using the Model Context Protocol, so you can analyze scraped data, extract insights, and generate responses with full control.

- **URL**: https://apify.com/lenticular\_negative/ollama-apify-mcp.md
- **Developed by:** [Anwesh Mishra](https://apify.com/lenticular_negative) (community)
- **Categories:** AI, Agents, MCP servers
- **Stats:** 6 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.01 / 1,000 results

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Ollama-Apify-MCP

Bring powerful **local AI** into your Apify workflows.

This project connects **Ollama’s locally-run language models** with the **Model Context Protocol (MCP)** and Apify’s scraping & automation platform. It enables you to process scraped data, extract insights, and generate intelligent responses — all **without external APIs**.

***

### 🧠 Overview

The **Ollama-Apify-MCP Actor** bridges Apify workflows with local LLMs via MCP, allowing AI-driven analysis and reasoning while preserving privacy and reducing costs.

***

### 🚀 Key Features

- 🔗 **Local LLM integration** — Run models like *Llama, Mistral, CodeLlama,* and more using Ollama
- 🧩 **MCP-based communication** — Standards-compliant protocol for tool interaction
- ⚙️ **Automatic context & preprocessing** — Improves model response quality
- 🛠️ **Extensible tool architecture** — Easily add custom MCP tools & resources
- 🔁 **Robust error handling & retries** — Reliable execution in workflows

***

### 📦 Quick Start

Use as in cursor, copilot, claude code or desktop

```
{
  "mcpServers": {
      "ollama-apify-mcp": {
        "url": "https://lenticular-negative--ollama-apify-mcp.apify.actor/mcp?token={YOUR_TOKEN}"
      }
    }
  }
```

#### 💻 Run Locally

```bash
pip install -r requirements.txt
APIFY_META_ORIGIN=STANDBY python -m src
```

Server runs at:

```
http://localhost:3000/mcp
```

***

#### ☁️ Deploy to Apify

1. Push the repo to GitHub
2. Add it as an Actor in Apify Console
3. Enable **Standby Mode**
4. Deploy

MCP endpoint:

```
https://lenticular-negative--ollama-apify-mcp.apify.actor/mcp
```

Include your API token:

```
Authorization: Bearer <APIFY_TOKEN>
```

### 🎯 Use Cases

- 📊 Analyze & summarize scraped web data
- 🔐 Privacy-first local LLM processing
- ⚡ Low-latency on-device inference
- 🧱 Build AI tools inside Apify workflows

***

### 🧩 Requirements

- Python 3.7+
- Ollama installed locally
- Apify CLI (for deployment)

***

### ❤️ Contributing

PRs and feature ideas are welcome — feel free to extend tools, improve docs, or share sample workflows.

***

### 📄 License

MIT License

# Actor input Schema

## Actor input object example

```json
{}
```

# Actor output Schema

## `mcpEndpoint` (type: `string`):

URL to access the MCP server endpoint

## `overview` (type: `string`):

All MCP tool call interactions and results

## `models` (type: `string`):

List of available Ollama models

## `generations` (type: `string`):

Text generation results from prompts

## `chats` (type: `string`):

Chat conversation results

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("lenticular_negative/ollama-apify-mcp").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("lenticular_negative/ollama-apify-mcp").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call lenticular_negative/ollama-apify-mcp --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,lenticular_negative/ollama-apify-mcp"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/cPef9y4ZEXbbyK8kc/builds/rwUEsU52XADZTfExm/openapi.json